The aim of this proposal is to develop NCELLIQ, Neuron and Cellular Imaging Quantitator, in order to assess quantitatively neurite loss and outgrowth screened by high throughput, automated fluorescent microscopy imaging. Such a tool will be essential in high content screening (HCS) of neuro-based assays. Loss of neuronal projections in Alzheimer's disease (AD) can be modeled in vitro in primary mouse cortical neuron cultures treated with the amyloid beta peptide, which has been shown to be a major cause of neurodegeneration in AD patients. As is the case in vivo, neurite loss precedes neuronal death in this disease model, which can be assessed visually either in live neurons through bright field microscopy, or through immunofluorescence following fixation and staining with neuronal marker class III tubulin beta antibody. Given recent advances in automated microscopy, the later visualization technique could be adapted for use in HCS of chemical libraries in order to identify compounds that can specifically suppress amyloid-induced neurite damage and loss. The hypothesis of this proposal is that the HCS informatics system developed, NCELLIQ, will be an important image processing and analytic tool to help identify possible drugs in treating Alzheimer's disease. Using multi-channel image data obtained from hippocampal neurons, we integrate and develop techniques to screen for potential drug leads in AD treatment. To test the hypothesis, we aim to define the high throughput image processing pipeline of NCELLIQ, develop automated algorithms for neurite centerline extraction and cellular image analysis, implement computational modeling tools, and evaluate the utility of the NCELLIQ with established, novel, and well-defined biology-driven experiments. NCELLIQ provides three key technical contributions. First, NCELLIQ will provide an integrated neural image processing pipeline using advanced computational algorithms to extract image contents of neuron- based screening assay automatically. Second, it will develop an innovative, effective, and fully automatic neurite centerline extraction methods using detector of curvilinear structures and dynamic programming. Third, it will develop mathematical representation of compound vector, as well as an innovative and effective scoring method to allow intuitive comprehension of the HCS results. The success of NCELLIQ will lead to a new class of bioinformatics tools for identifying quality hits in AD drug development and for determining cyptological profile of neurites. Upon the successful completion of the project, we will set up a website to disseminate the NCELLIQ software and sample image datasets.